梯度下降法

梯度下降法

(一)算法介紹

(二)python代碼實現

class GD():
    def __init__(self, objFunction, GDFunction, accurate):
        self.objFunction = objFunction
        self.GDFuction = GDFunction
        self.accurate = accurate
        self.lamta = 0.01

    def GDFunction(self):
        x = 1
        self.objFunction_value = eval(self.objFunction)
        while eval(self.GDFuction) > self.accurate:
            x = x - self.lamta * eval(self.GDFuction)
            self.objFunction_current_value = eval(self.objFunction)
            if abs(self.objFunction_current_value - self.objFunction_value) < self.accurate:
                break
            else:
                self.objFunction_value = self.objFunction_current_value
        return x, self.objFunction_value

if __name__ == "__main__":
    objFunction = input('Please input object Function:')
    GDFunction = input('Please input gradient descent:')
    # accurate = float(input('Please input accurate.'))
    # objFunction = "(x-1) ** 2 + 1"
    # GDFunction = "2 * (x - 1)"
    accurate = 0.00000001
    GD = GD(objFunction, GDFunction, accurate)
    print("x = %.2f, min_value = %.2f" % GD.GDFunction())